There exist state-of-the-art inference algorithms for exact inference [1]} in discrete models. These algorithms are based on the knowledge compilation technique [2]} that uses logical reasoning to exploit CSIs. However, for many models, exact inference quickly becomes infeasible. Stochastic sampling for approximate inference is a standard solution. Sampling algorithms are simple yet powerful tools, and they can be applied to arbitrary complex hybrid models, unlike exact inference. It is widely believed that CSI properties in distributions are difficult to exploit for approximate inference [1]}. To solve this difficult problem, we introduce the context-specific likelihood weighting (CS-LW), a sampling algorithm that exploits both CI and CSI properties, leading to faster convergence and faster sampling speed than standard likelihood weighting (LW).
